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eBR Critical Analysis - Malabo Policy Learning Event 2020

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A presentation by Dr. Racine Ly, Director, Data Management, Digital Products and Technology

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eBR Critical Analysis - Malabo Policy Learning Event 2020

  1. 1. e-BR Critical Analysis Racine Ly, PhD. Director, Data Management, Digital Products and Technology AKADEMIYA2063, Kigali, Rwanda
  2. 2. Summary 1. What is the eBR? 2. eBR Noticed Common Errors 3. Planned Improvements 4. Perspectives 12/21/20 eBR Critical Analysis 2
  3. 3. 1. What is the eBR? • The eBR is an online data gathering system that has been designed and is being maintained by the ReSAKSS team; • The main purpose of the eBR is to collect the relevant data for each BR cycle from countries under the supervision of ReCs and the African Union; • At each level, a validation is required to move to the next step; • The ReSAKSS is also responsible for generating the eBR score card automatically from the data collection system; 12/21/20 eBR Critical Analysis 3
  4. 4. 2. eBR Noticed Common Errors 12/21/20 eBR Critical Analysis 4 • Wrong units of measurement e.g. Thousand vs. Million vs. Billion • Values that are too high or low (missing digits, space between the digits). e.g. 1 234 567 (text) instead of 1234567 (number) • Confusion in the use of decimal points. e.g. 1.234.567 (decimal points) instead of 1,234,567 (with comas) • Inconsistent data where the sum of parts is greater or less than the aggregate value. e.g. a + b + c = 10, while a = 6, b = 5 and c = 4 is entered • Illogical responses e.g. When the response to a subsequent question is supposed to be conditioned by the preceding question’s answer. • Data conflict with the data source
  5. 5. 3. Planned Improvements Objective 1: Reinforce the integrated warning system on data entered, and an automated notification system. 12/21/20 eBR Critical Analysis 5 • Embed a format error identifier and notify the operator for correction • Prevent the system to notify ReCs until the error has been resolved • Convention to adopt for numerical data (decimals)? Objective 2: Improve the eBR to flag unusual trends for correction and comment • Compare data entered with previous BR cycle and identify possible outliers with threshold exceeding limit, anomaly detection algorithm, and Benford’s law; Objective 3: Improve data traceability by requesting detailed and mandatory data source information • Introduce a data source window with mandatory entries to ensure data traceability
  6. 6. 4. Perspectives • The team is working on developing a survey to gather more information for improvement about the eBR, with several stakeholders such as countries, ReCs, AU, and External users. • It is planned to train countries on the different features that will be added to the eBR version of the current cycle. The eBR as a Decision Support Tool • There is an ongoing project of expanding the BR work to a digital decision support tool with underlying mathematical modeling • The tool will allow one to identify possible levers to achieve a specific goal based on the data collected from the previous and current BR cycle. 12/21/20 eBR Critical Analysis 6 THANK YOU